Add to ORKGThis work presents several ideas for planning under uncertainty. We seek to recycle electromechanical devices with a robotic arm. We resort to the Markov Decision Process formulation. In order to avoid scalability issues, we employ determinization techniques and hierarchical planning
Many robotic tasks, such as mobile manipulation, often require interaction with unstructured environ...
Autonomous robotic systems evolve in unpredictable environments, and have to deal with sensor uncert...
Colloque avec actes et comité de lecture. internationale.International audienceMarkov Decision Proce...
This work presents several ideas for planning under uncertainty. We seek to recycle electromechanica...
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for a...
In this letter, we focus on finding practical resolution methods for Markov decision processes (MDPs...
This thesis experimentally addresses the issue of planning under uncertainty in robotics, with refer...
Due to the high complexity of probabilistic planning algorithms, roboticists often opt for determini...
Motion planning in uncertain and dynamic environments is critical for reliable operation of autonomo...
Robots are being employed not only for assembly tasks, but also in domains like healthcare, mining, ...
Thesis (Ph.D.)--University of Washington, 2013The ability to plan in the presence of uncertainty abo...
Robots acting in human-scale environments must plan under uncertainty in large state–action spaces a...
We provide a method, based on the theory of Markov decision problems, for efficient planning in stoc...
Robotic technologies have advanced significantly that improved capabilities of robots. Such robots o...
Partially Observable Markov Decision Processes (POMDPs) define a useful formalism to express probabi...
Many robotic tasks, such as mobile manipulation, often require interaction with unstructured environ...
Autonomous robotic systems evolve in unpredictable environments, and have to deal with sensor uncert...
Colloque avec actes et comité de lecture. internationale.International audienceMarkov Decision Proce...
This work presents several ideas for planning under uncertainty. We seek to recycle electromechanica...
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for a...
In this letter, we focus on finding practical resolution methods for Markov decision processes (MDPs...
This thesis experimentally addresses the issue of planning under uncertainty in robotics, with refer...
Due to the high complexity of probabilistic planning algorithms, roboticists often opt for determini...
Motion planning in uncertain and dynamic environments is critical for reliable operation of autonomo...
Robots are being employed not only for assembly tasks, but also in domains like healthcare, mining, ...
Thesis (Ph.D.)--University of Washington, 2013The ability to plan in the presence of uncertainty abo...
Robots acting in human-scale environments must plan under uncertainty in large state–action spaces a...
We provide a method, based on the theory of Markov decision problems, for efficient planning in stoc...
Robotic technologies have advanced significantly that improved capabilities of robots. Such robots o...
Partially Observable Markov Decision Processes (POMDPs) define a useful formalism to express probabi...
Many robotic tasks, such as mobile manipulation, often require interaction with unstructured environ...
Autonomous robotic systems evolve in unpredictable environments, and have to deal with sensor uncert...
Colloque avec actes et comité de lecture. internationale.International audienceMarkov Decision Proce...